With data-intensive applications, scaling out databases is the norm. As a result, these database deployments are often awash in clusters of small nodes, which bring with them many hidden costs. For example, scaling out usually comes at the cost of resource efficiency, since it can lead to low resource utilization.
So why is scaling out databases so common? Scaled out deployment architectures are based on several assumptions that we’ll examine in this white paper. We’ll demonstrate that these assumptions prove unfounded for database infrastructure that is able to take advantage of the rich computing resources available on large nodes. In the process, we’ll show that Scylla is uniquely positioned to leverage the multi-core architecture that modern cloud platforms offer, making it not only possible, but also preferable, to use smaller clusters of large nodes for Big Data deployments.
Read to learn the following:
- Understand the benefits and limitations of scaling up vs scaling out, including cost implications, performance differences, and operational complexity.
- Review benchmark data and results that challenge common assumptions about the inefficiencies of large-node clusters in distributed databases.
- Gain insights into how to evaluate your database's needs to select the most effective database scaling strategy for your specific workload.
